Classification and Identification of Primitive Kharif Crops using Supervised Deep Convolutional Networks

Abstract

BackgroundThe severity of diseases and threats found in different crop varieties is one of the primary causes of degradation in the agricultural economy. Early detection and disease diagnosis in crops will facilitate farmers to improve their livelihood and mankind. ObjectiveThis study aimed to develop method for disease identification in several seasonal crops during their early stages using deep learning architectures i.e. convolutional neural networks (CNN) and compare the feasibility, accuracy and performance of the proposed network with conventional feature extraction techniques like support vector machine, k-nearest neighbor, genetic algorithm, and artificial neural networks. MethodThis study preferred a database of 600 images i.e. 200 images of individual crop varieties which are labeled with 10 kinds of crop diseases. Each crop varieties have two different kinds of classes i.e. health crop and rusty crop. The CNNs are trained in such a manner that it will be able to detect diseases from infected crop varieties. ResultDifferent convolution filters and pooling types of different sizes are used in the proposed work. Max pooling with a filter size of 32*32*3 achieves the accuracy of 92%. Average pool size with a convolution filter size of 64*64*3 achieved maximum accuracy of 93.7% and gains the better results in comparison to other machine learning and feature extraction models. ConclusionsThe contribution of the proposed work could be summarized as: (i) obtained results shows improvement in the feasibility and performance of CNN over other machine learning models. (ii) High performance shows the immediate crop disease identification ability of deep learning techniques over the different feature extraction models.